{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Real time linkage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we demonstrate splink's incremental and real time linkage capabilities - specifically:\n",
    "- the `linker.compare_two_records` function, that allows you to interactively explore the results of a linkage model; and\n",
    "- the `linker.find_matches_to_new_records` that allows you to incrementally find matches to a small number of new records"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: Load a pre-trained linkage model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import json\n",
    "from splink.duckdb.duckdb_linker import DuckDBLinker\n",
    "import altair as alt\n",
    "alt.renderers.enable('mimetype')\n",
    "\n",
    "with open(\"demo_settings/real_time_settings.json\") as f:\n",
    "    trained_settings = json.load(f)\n",
    "\n",
    "df = pd.read_csv(\"./data/fake_1000.csv\")\n",
    "\n",
    "linker = DuckDBLinker(df, trained_settings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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          "type": "rule",
          "x2Offset": 30,
          "xOffset": -30
         }
        }
       ],
       "params": [
        {
         "bind": {
          "input": "range",
          "max": 1,
          "min": 0,
          "step": 1
         },
         "description": "Filter by the interation number",
         "name": "record_number",
         "value": 0
        }
       ],
       "resolve": {
        "axis": {
         "y": "independent"
        }
       },
       "title": {
        "subtitle": "How each comparison contributes to the final match score",
        "text": "Match weights waterfall chart"
       },
       "transform": [
        {
         "filter": "(datum.record_number == record_number)"
        },
        {
         "filter": "(datum.bayes_factor !== 1.0)"
        },
        {
         "frame": [
          null,
          0
         ],
         "window": [
          {
           "as": "sum",
           "field": "log2_bayes_factor",
           "op": "sum"
          },
          {
           "as": "lead",
           "field": "column_name",
           "op": "lead"
          }
         ]
        },
        {
         "as": "sum",
         "calculate": "datum.column_name === \"Final score\" ? datum.sum - datum.log2_bayes_factor : datum.sum"
        },
        {
         "as": "lead",
         "calculate": "datum.lead === null ? datum.column_name : datum.lead"
        },
        {
         "as": "previous_sum",
         "calculate": "datum.column_name === \"Final score\" || datum.column_name === \"Prior match weight\" ? 0 : datum.sum - datum.log2_bayes_factor"
        },
        {
         "as": "top_label",
         "calculate": "datum.sum > datum.previous_sum ? datum.column_name : \"\""
        },
        {
         "as": "bottom_label",
         "calculate": "datum.sum < datum.previous_sum ? datum.column_name : \"\""
        },
        {
         "as": "sum_top",
         "calculate": "datum.sum > datum.previous_sum ? datum.sum : datum.previous_sum"
        },
        {
         "as": "sum_bottom",
         "calculate": "datum.sum < datum.previous_sum ? datum.sum : datum.previous_sum"
        },
        {
         "as": "center",
         "calculate": "(datum.sum + datum.previous_sum) / 2"
        },
        {
         "as": "text_log2_bayes_factor",
         "calculate": "(datum.log2_bayes_factor > 0 ? \"+\" : \"\") + datum.log2_bayes_factor"
        },
        {
         "as": "dy",
         "calculate": "datum.sum < datum.previous_sum ? 4 : -4"
        },
        {
         "as": "baseline",
         "calculate": "datum.sum < datum.previous_sum ? \"top\" : \"bottom\""
        },
        {
         "as": "prob",
         "calculate": "1. / (1 + pow(2, -1.*datum.sum))"
        },
        {
         "as": "zero",
         "calculate": "0*datum.sum"
        }
       ],
       "width": {
        "step": 75
       }
      },
      "image/png": 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",
      "text/plain": [
       "<VegaLite 4 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linker.waterfall_chart(linker.predict().as_record_dict(limit=2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step  Comparing two records\n",
    "\n",
    "It's now possible to compute a match weight for any two records using `linker.compare_two_records()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>match_weight</th>\n",
       "      <th>match_probability</th>\n",
       "      <th>unique_id_l</th>\n",
       "      <th>unique_id_r</th>\n",
       "      <th>first_name_l</th>\n",
       "      <th>first_name_r</th>\n",
       "      <th>gamma_first_name</th>\n",
       "      <th>tf_first_name_l</th>\n",
       "      <th>tf_first_name_r</th>\n",
       "      <th>bf_first_name</th>\n",
       "      <th>...</th>\n",
       "      <th>tf_city_r</th>\n",
       "      <th>bf_city</th>\n",
       "      <th>bf_tf_adj_city</th>\n",
       "      <th>email_l</th>\n",
       "      <th>email_r</th>\n",
       "      <th>gamma_email</th>\n",
       "      <th>tf_email_l</th>\n",
       "      <th>tf_email_r</th>\n",
       "      <th>bf_email</th>\n",
       "      <th>bf_tf_adj_email</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13.161672</td>\n",
       "      <td>0.999891</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Lucas</td>\n",
       "      <td>Lucas</td>\n",
       "      <td>2</td>\n",
       "      <td>0.001203</td>\n",
       "      <td>0.001203</td>\n",
       "      <td>87.571229</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.446404</td>\n",
       "      <td>1.0</td>\n",
       "      <td>lucas.smith@hotmail.com</td>\n",
       "      <td>lucas.smith@hotmail.com</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>263.229168</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   match_weight  match_probability  unique_id_l  unique_id_r first_name_l  \\\n",
       "0     13.161672           0.999891            1            2        Lucas   \n",
       "\n",
       "  first_name_r  gamma_first_name  tf_first_name_l  tf_first_name_r  \\\n",
       "0        Lucas                 2         0.001203         0.001203   \n",
       "\n",
       "   bf_first_name  ...  tf_city_r   bf_city bf_tf_adj_city  \\\n",
       "0      87.571229  ...        NaN  0.446404            1.0   \n",
       "\n",
       "                   email_l                  email_r  gamma_email  tf_email_l  \\\n",
       "0  lucas.smith@hotmail.com  lucas.smith@hotmail.com            1         NaN   \n",
       "\n",
       "   tf_email_r    bf_email bf_tf_adj_email  \n",
       "0         NaN  263.229168             1.0  \n",
       "\n",
       "[1 rows x 39 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from splink.term_frequencies import compute_term_frequencies_from_concat_with_tf\n",
    "record_1  = {\n",
    "     'unique_id':1,\n",
    "     'first_name': \"Lucas\",\n",
    "     'surname': \"Smith\",\n",
    "     'dob': \"1984-01-02\",\n",
    "     'city': \"London\",\n",
    "     'email': \"lucas.smith@hotmail.com\"\n",
    "}\n",
    "\n",
    "record_2  = {\n",
    "     'unique_id':2,\n",
    "     'first_name': \"Lucas\",\n",
    "     'surname': \"Smith\",\n",
    "     'dob': \"1983-02-12\",\n",
    "     'city': \"Machester\",\n",
    "     'email': \"lucas.smith@hotmail.com\"\n",
    "}\n",
    "\n",
    "linker._settings_obj_._retain_intermediate_calculation_columns = True\n",
    "linker._settings_obj_._retain_matching_columns = True\n",
    "\n",
    "linker.compute_tf_table(\"first_name\")\n",
    "linker.compute_tf_table(\"surname\")\n",
    "linker.compute_tf_table(\"dob\")\n",
    "linker.compute_tf_table(\"city\")\n",
    "linker.compute_tf_table(\"email\")\n",
    "\n",
    "\n",
    "df_two = linker.compare_two_records(record_1, record_2)\n",
    "df_two.as_pandas_dataframe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: Interactive comparisons\n",
    "\n",
    "One interesting applicatin of `compare_two_records` is to create a simple interface that allows the user to input two records interactively, and get real time feedback.\n",
    "\n",
    "In the following cell we use `ipywidets` for this purpose.  ✨✨ Change the values in the text boxes to see the waterfall chart update in real time. ✨✨"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6995eb3b42dc48f4839fe608b431129d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(VBox(children=(Text(value='1', description='unique_id'), Text(value='Lucas', description='first…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6869c5e70a5c4b69a2bcac852ee6e686",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import ipywidgets as widgets\n",
    "fields = [\"unique_id\", \"first_name\",\"surname\",\"dob\",\"email\",\"city\"]\n",
    "\n",
    "left_text_boxes = []\n",
    "right_text_boxes = []\n",
    "\n",
    "inputs_to_interactive_output = {}\n",
    "\n",
    "for f in fields:\n",
    "    wl = widgets.Text(description=f, value =str(record_1[f]))\n",
    "    left_text_boxes.append(wl)\n",
    "    inputs_to_interactive_output[f\"{f}_l\"] = wl\n",
    "    wr = widgets.Text( description=f, value =str(record_2[f]))\n",
    "    right_text_boxes.append(wr)\n",
    "    inputs_to_interactive_output[f\"{f}_r\"] = wr\n",
    "\n",
    "\n",
    "b1 = widgets.VBox(left_text_boxes)\n",
    "b2 = widgets.VBox(right_text_boxes)\n",
    "ui = widgets.HBox([b1,b2])\n",
    "\n",
    "def myfn(**kwargs):\n",
    "    my_args = dict(kwargs)\n",
    "    \n",
    "    record_left = {}\n",
    "    record_right = {}\n",
    "    \n",
    "    for key, value in my_args.items():\n",
    "        if value == '':\n",
    "            value = None\n",
    "        if key.endswith(\"_l\"):\n",
    "            record_left[key[:-2]] = value\n",
    "        if key.endswith(\"_r\"):\n",
    "            record_right[key[:-2]] = value\n",
    "            \n",
    "\n",
    "    linker._settings_obj_._retain_intermediate_calculation_columns = True\n",
    "    linker._settings_obj_._retain_matching_columns = True\n",
    "\n",
    "    df_two = linker.compare_two_records(record_left, record_right)\n",
    "\n",
    "    recs = df_two.as_pandas_dataframe().to_dict(orient=\"records\")\n",
    "    from splink.charts import waterfall_chart\n",
    "    display(linker.waterfall_chart(recs, filter_nulls=False))\n",
    "\n",
    "\n",
    "out = widgets.interactive_output(myfn, inputs_to_interactive_output)\n",
    "\n",
    "display(ui,out)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finding matching records interactively\n",
    "\n",
    "It is also possible to search the records in the input dataset rapidly using the `linker.find_matches_to_new_records()` function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>match_weight</th>\n",
       "      <th>match_probability</th>\n",
       "      <th>unique_id_l</th>\n",
       "      <th>unique_id_r</th>\n",
       "      <th>first_name_l</th>\n",
       "      <th>first_name_r</th>\n",
       "      <th>gamma_first_name</th>\n",
       "      <th>tf_first_name_l</th>\n",
       "      <th>tf_first_name_r</th>\n",
       "      <th>bf_first_name</th>\n",
       "      <th>...</th>\n",
       "      <th>tf_city_r</th>\n",
       "      <th>bf_city</th>\n",
       "      <th>bf_tf_adj_city</th>\n",
       "      <th>email_l</th>\n",
       "      <th>email_r</th>\n",
       "      <th>gamma_email</th>\n",
       "      <th>tf_email_l</th>\n",
       "      <th>tf_email_r</th>\n",
       "      <th>bf_email</th>\n",
       "      <th>bf_tf_adj_email</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.531793</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>123987</td>\n",
       "      <td>Robert</td>\n",
       "      <td>Robert</td>\n",
       "      <td>2</td>\n",
       "      <td>0.003610</td>\n",
       "      <td>0.00361</td>\n",
       "      <td>87.571229</td>\n",
       "      <td>...</td>\n",
       "      <td>0.212792</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>robert255@smith.net</td>\n",
       "      <td>robert255@smith.net</td>\n",
       "      <td>1</td>\n",
       "      <td>0.001267</td>\n",
       "      <td>0.001267</td>\n",
       "      <td>263.229168</td>\n",
       "      <td>1.730964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14.550320</td>\n",
       "      <td>0.999958</td>\n",
       "      <td>1</td>\n",
       "      <td>123987</td>\n",
       "      <td>Robert</td>\n",
       "      <td>Robert</td>\n",
       "      <td>2</td>\n",
       "      <td>0.003610</td>\n",
       "      <td>0.00361</td>\n",
       "      <td>87.571229</td>\n",
       "      <td>...</td>\n",
       "      <td>0.212792</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>roberta25@smith.net</td>\n",
       "      <td>robert255@smith.net</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002535</td>\n",
       "      <td>0.001267</td>\n",
       "      <td>0.423438</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.388623</td>\n",
       "      <td>0.999255</td>\n",
       "      <td>3</td>\n",
       "      <td>123987</td>\n",
       "      <td>Robert</td>\n",
       "      <td>Robert</td>\n",
       "      <td>2</td>\n",
       "      <td>0.003610</td>\n",
       "      <td>0.00361</td>\n",
       "      <td>87.571229</td>\n",
       "      <td>...</td>\n",
       "      <td>0.212792</td>\n",
       "      <td>0.446404</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>robert255@smith.net</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.001267</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.427256</td>\n",
       "      <td>0.843228</td>\n",
       "      <td>2</td>\n",
       "      <td>123987</td>\n",
       "      <td>Rob</td>\n",
       "      <td>Robert</td>\n",
       "      <td>0</td>\n",
       "      <td>0.001203</td>\n",
       "      <td>0.00361</td>\n",
       "      <td>0.218767</td>\n",
       "      <td>...</td>\n",
       "      <td>0.212792</td>\n",
       "      <td>10.484859</td>\n",
       "      <td>0.259162</td>\n",
       "      <td>roberta25@smith.net</td>\n",
       "      <td>robert255@smith.net</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002535</td>\n",
       "      <td>0.001267</td>\n",
       "      <td>0.423438</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-2.123090</td>\n",
       "      <td>0.186697</td>\n",
       "      <td>8</td>\n",
       "      <td>123987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Robert</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00361</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.212792</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>robert255@smith.net</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.001267</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-2.205894</td>\n",
       "      <td>0.178139</td>\n",
       "      <td>754</td>\n",
       "      <td>123987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Robert</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00361</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.212792</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>j.c@whige.wort</td>\n",
       "      <td>robert255@smith.net</td>\n",
       "      <td>0</td>\n",
       "      <td>0.001267</td>\n",
       "      <td>0.001267</td>\n",
       "      <td>0.423438</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2.802309</td>\n",
       "      <td>0.125383</td>\n",
       "      <td>750</td>\n",
       "      <td>123987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Robert</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00361</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.212792</td>\n",
       "      <td>10.484859</td>\n",
       "      <td>0.259162</td>\n",
       "      <td>j.c@white.org</td>\n",
       "      <td>robert255@smith.net</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002535</td>\n",
       "      <td>0.001267</td>\n",
       "      <td>0.423438</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   match_weight  match_probability  unique_id_l  unique_id_r first_name_l  \\\n",
       "3     23.531793           1.000000            0       123987       Robert   \n",
       "4     14.550320           0.999958            1       123987       Robert   \n",
       "2     10.388623           0.999255            3       123987       Robert   \n",
       "1      2.427256           0.843228            2       123987          Rob   \n",
       "5     -2.123090           0.186697            8       123987          NaN   \n",
       "6     -2.205894           0.178139          754       123987          NaN   \n",
       "0     -2.802309           0.125383          750       123987          NaN   \n",
       "\n",
       "  first_name_r  gamma_first_name  tf_first_name_l  tf_first_name_r  \\\n",
       "3       Robert                 2         0.003610          0.00361   \n",
       "4       Robert                 2         0.003610          0.00361   \n",
       "2       Robert                 2         0.003610          0.00361   \n",
       "1       Robert                 0         0.001203          0.00361   \n",
       "5       Robert                -1              NaN          0.00361   \n",
       "6       Robert                -1              NaN          0.00361   \n",
       "0       Robert                -1              NaN          0.00361   \n",
       "\n",
       "   bf_first_name  ...  tf_city_r    bf_city bf_tf_adj_city  \\\n",
       "3      87.571229  ...   0.212792   1.000000       1.000000   \n",
       "4      87.571229  ...   0.212792   1.000000       1.000000   \n",
       "2      87.571229  ...   0.212792   0.446404       1.000000   \n",
       "1       0.218767  ...   0.212792  10.484859       0.259162   \n",
       "5       1.000000  ...   0.212792   1.000000       1.000000   \n",
       "6       1.000000  ...   0.212792   1.000000       1.000000   \n",
       "0       1.000000  ...   0.212792  10.484859       0.259162   \n",
       "\n",
       "               email_l              email_r  gamma_email  tf_email_l  \\\n",
       "3  robert255@smith.net  robert255@smith.net            1    0.001267   \n",
       "4  roberta25@smith.net  robert255@smith.net            0    0.002535   \n",
       "2                  NaN  robert255@smith.net           -1         NaN   \n",
       "1  roberta25@smith.net  robert255@smith.net            0    0.002535   \n",
       "5                  NaN  robert255@smith.net           -1         NaN   \n",
       "6       j.c@whige.wort  robert255@smith.net            0    0.001267   \n",
       "0        j.c@white.org  robert255@smith.net            0    0.002535   \n",
       "\n",
       "   tf_email_r    bf_email bf_tf_adj_email  \n",
       "3    0.001267  263.229168        1.730964  \n",
       "4    0.001267    0.423438        1.000000  \n",
       "2    0.001267    1.000000        1.000000  \n",
       "1    0.001267    0.423438        1.000000  \n",
       "5    0.001267    1.000000        1.000000  \n",
       "6    0.001267    0.423438        1.000000  \n",
       "0    0.001267    0.423438        1.000000  \n",
       "\n",
       "[7 rows x 39 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "record = {'unique_id': 123987,\n",
    " 'first_name': \"Robert\",\n",
    " 'surname': \"Alan\",\n",
    " 'dob': \"1971-05-24\",\n",
    " 'city': \"London\",\n",
    " 'email': \"robert255@smith.net\"\n",
    "}\n",
    "\n",
    "\n",
    "\n",
    "df_inc = linker.find_matches_to_new_records([record], blocking_rules=[]).as_pandas_dataframe()\n",
    "df_inc.sort_values(\"match_weight\", ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interactive interface for finding records\n",
    "\n",
    "Again, we can use `ipywidgets` to build an interactive interface for the `linker.find_matches_to_new_records` function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "484a1f66dbdb457bac7d3720c0b10110",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Text(value='Robert', description='first_name'), Text(value='Alan', description='surname'…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from splink.charts import waterfall_chart\n",
    "\n",
    "@widgets.interact(first_name='Robert', surname=\"Alan\", dob=\"1971-05-24\", city=\"London\", email=\"robert255@smith.net\")\n",
    "def interactive_link(first_name, surname, dob, city, email):    \n",
    "\n",
    "    record = {'unique_id': 123987,\n",
    "     'first_name': first_name,\n",
    "     'surname': surname,\n",
    "     'dob': dob,\n",
    "     'city': city,\n",
    "     'email': email,\n",
    "     'group': 0}\n",
    "\n",
    "    for key in record.keys():\n",
    "        if type(record[key]) == str:\n",
    "            if record[key].strip() == \"\":\n",
    "                record[key] = None\n",
    "\n",
    "    \n",
    "    df_inc = linker.find_matches_to_new_records([record], blocking_rules=[f\"(true)\"]).as_pandas_dataframe()\n",
    "    df_inc = df_inc.sort_values(\"match_weight\", ascending=False)\n",
    "    recs = df_inc.to_dict(orient=\"records\")\n",
    "    \n",
    "\n",
    "\n",
    "    display(linker.waterfall_chart(recs, filter_nulls=False))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v4+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v5.2.json",
       "config": {
        "header": {
         "title": null
        },
        "mark": {
         "tooltip": null
        },
        "title": {
         "anchor": "middle"
        },
        "view": {
         "height": 60,
         "width": 400
        }
       },
       "data": {
        "values": [
         {
          "bayes_factor": 0.023816582302252427,
          "bayes_factor_description": "The probability that two random records drawn at random match is 0.023 or one in  43.0 records.This is equivalent to a starting match weight of -5.392.",
          "comparison_name": "probability_two_random_records_match",
          "comparison_sort_order": -1,
          "comparison_vector_value": 0,
          "has_tf_adjustments": false,
          "is_null_level": false,
          "label_for_charts": "",
          "log2_bayes_factor": -5.391889789559854,
          "m_probability": null,
          "m_probability_description": null,
          "max_comparison_vector_value": 0,
          "probability_two_random_records_match": 0.02326254791526835,
          "sql_condition": null,
          "tf_adjustment_column": null,
          "tf_adjustment_weight": null,
          "u_probability": null,
          "u_probability_description": null
         },
         {
          "bayes_factor": 87.57122888658395,
          "bayes_factor_description": "If comparison level is `exact_match` then comparison is 87.57 times more likely to be a match",
          "comparison_name": "first_name",
          "comparison_sort_order": 0,
          "comparison_vector_value": 2,
          "has_tf_adjustments": true,
          "is_null_level": false,
          "label_for_charts": "exact_match",
          "log2_bayes_factor": 6.452385051922501,
          "m_probability": 0.5073501669215337,
          "m_probability_description": "Amongst matching record comparisons, 50.74% of records are in the exact_match comparison level",
          "max_comparison_vector_value": 2,
          "probability_two_random_records_match": 0.02326254791526835,
          "sql_condition": "first_name_l = first_name_r",
          "tf_adjustment_column": "first_name",
          "tf_adjustment_weight": 1,
          "u_probability": 0.0057935713975033705,
          "u_probability_description": "Amongst non-matching record comparisons, 0.58% of records are in the exact_match comparison level"
         },
         {
          "bayes_factor": 27.407809072486973,
          "bayes_factor_description": "If comparison level is `levenstein <= 2` then comparison is 27.41 times more likely to be a match",
          "comparison_name": "first_name",
          "comparison_sort_order": 0,
          "comparison_vector_value": 1,
          "has_tf_adjustments": false,
          "is_null_level": false,
          "label_for_charts": "Levenstein <= 2",
          "log2_bayes_factor": 4.776515101395072,
          "m_probability": 0.27736434159157797,
          "m_probability_description": "Amongst matching record comparisons, 27.74% of records are in the levenstein <= 2 comparison level",
          "max_comparison_vector_value": 2,
          "probability_two_random_records_match": 0.02326254791526835,
          "sql_condition": "levenshtein(first_name_l, first_name_r) <= 2",
          "tf_adjustment_column": null,
          "tf_adjustment_weight": 1,
          "u_probability": 0.010119901990634016,
          "u_probability_description": "Amongst non-matching record comparisons, 1.01% of records are in the levenstein <= 2 comparison level"
         },
         {
          "bayes_factor": 0.21876683164040506,
          "bayes_factor_description": "If comparison level is `all other comparisons` then comparison is  4.57 times less likely to be a match",
          "comparison_name": "first_name",
          "comparison_sort_order": 0,
          "comparison_vector_value": 0,
          "has_tf_adjustments": false,
          "is_null_level": false,
          "label_for_charts": "All other comparisons",
          "log2_bayes_factor": -2.1925340745596764,
          "m_probability": 0.21528549148688833,
          "m_probability_description": "Amongst matching record comparisons, 21.53% of records are in the all other comparisons comparison level",
          "max_comparison_vector_value": 2,
          "probability_two_random_records_match": 0.02326254791526835,
          "sql_condition": "ELSE",
          "tf_adjustment_column": null,
          "tf_adjustment_weight": 1,
          "u_probability": 0.9840865266118626,
          "u_probability_description": "Amongst non-matching record comparisons, 98.41% of records are in the all other comparisons comparison level"
         },
         {
          "bayes_factor": 92.38584465067325,
          "bayes_factor_description": "If comparison level is `exact_match` then comparison is 92.39 times more likely to be a match",
          "comparison_name": "surname",
          "comparison_sort_order": 1,
          "comparison_vector_value": 2,
          "has_tf_adjustments": true,
          "is_null_level": false,
          "label_for_charts": "exact_match",
          "log2_bayes_factor": 6.529599913880287,
          "m_probability": 0.4517645215191846,
          "m_probability_description": "Amongst matching record comparisons, 45.18% of records are in the exact_match comparison level",
          "max_comparison_vector_value": 2,
          "probability_two_random_records_match": 0.02326254791526835,
          "sql_condition": "surname_l = surname_r",
          "tf_adjustment_column": "surname",
          "tf_adjustment_weight": 1,
          "u_probability": 0.004889975550122249,
          "u_probability_description": "Amongst non-matching record comparisons, 0.49% of records are in the exact_match comparison level"
         },
         {
          "bayes_factor": 41.74477904659683,
          "bayes_factor_description": "If comparison level is `levenstein <= 2` then comparison is 41.74 times more likely to be a match",
          "comparison_name": "surname",
          "comparison_sort_order": 1,
          "comparison_vector_value": 1,
          "has_tf_adjustments": false,
          "is_null_level": false,
          "label_for_charts": "Levenstein <= 2",
          "log2_bayes_factor": 5.383523868172574,
          "m_probability": 0.3078165102205689,
          "m_probability_description": "Amongst matching record comparisons, 30.78% of records are in the levenstein <= 2 comparison level",
          "max_comparison_vector_value": 2,
          "probability_two_random_records_match": 0.02326254791526835,
          "sql_condition": "levenshtein(surname_l, surname_r) <= 2",
          "tf_adjustment_column": null,
          "tf_adjustment_weight": 1,
          "u_probability": 0.007373772654946249,
          "u_probability_description": "Amongst non-matching record comparisons, 0.74% of records are in the levenstein <= 2 comparison level"
         },
         {
          "bayes_factor": 0.24340401379756268,
          "bayes_factor_description": "If comparison level is `all other comparisons` then comparison is  4.11 times less likely to be a match",
          "comparison_name": "surname",
          "comparison_sort_order": 1,
          "comparison_vector_value": 0,
          "has_tf_adjustments": false,
          "is_null_level": false,
          "label_for_charts": "All other comparisons",
          "log2_bayes_factor": -2.03857513621085,
          "m_probability": 0.24041896826024636,
          "m_probability_description": "Amongst matching record comparisons, 24.04% of records are in the all other comparisons comparison level",
          "max_comparison_vector_value": 2,
          "probability_two_random_records_match": 0.02326254791526835,
          "sql_condition": "ELSE",
          "tf_adjustment_column": null,
          "tf_adjustment_weight": 1,
          "u_probability": 0.9877362517949315,
          "u_probability_description": "Amongst non-matching record comparisons, 98.77% of records are in the all other comparisons comparison level"
         },
         {
          "bayes_factor": 232.03049287476935,
          "bayes_factor_description": "If comparison level is `exact_match` then comparison is 232.03 times more likely to be a match",
          "comparison_name": "dob",
          "comparison_sort_order": 2,
          "comparison_vector_value": 2,
          "has_tf_adjustments": true,
          "is_null_level": false,
          "label_for_charts": "exact_match",
          "log2_bayes_factor": 7.858170603008739,
          "m_probability": 0.405530771330678,
          "m_probability_description": "Amongst matching record comparisons, 40.55% of records are in the exact_match comparison level",
          "max_comparison_vector_value": 2,
          "probability_two_random_records_match": 0.02326254791526835,
          "sql_condition": "dob_l = dob_r",
          "tf_adjustment_column": "dob",
          "tf_adjustment_weight": 1,
          "u_probability": 0.0017477477477477479,
          "u_probability_description": "Amongst non-matching record comparisons, 0.17% of records are in the exact_match comparison level"
         },
         {
          "bayes_factor": 21.492671620753597,
          "bayes_factor_description": "If comparison level is `levenstein <= 2` then comparison is 21.49 times more likely to be a match",
          "comparison_name": "dob",
          "comparison_sort_order": 2,
          "comparison_vector_value": 1,
          "has_tf_adjustments": false,
          "is_null_level": false,
          "label_for_charts": "Levenstein <= 2",
          "log2_bayes_factor": 4.425772921275592,
          "m_probability": 0.3679356056637918,
          "m_probability_description": "Amongst matching record comparisons, 36.79% of records are in the levenstein <= 2 comparison level",
          "max_comparison_vector_value": 2,
          "probability_two_random_records_match": 0.02326254791526835,
          "sql_condition": "levenshtein(dob_l, dob_r) <= 2",
          "tf_adjustment_column": null,
          "tf_adjustment_weight": 1,
          "u_probability": 0.01711911911911912,
          "u_probability_description": "Amongst non-matching record comparisons, 1.71% of records are in the levenstein <= 2 comparison level"
         },
         {
          "bayes_factor": 0.23088978993311773,
          "bayes_factor_description": "If comparison level is `all other comparisons` then comparison is  4.33 times less likely to be a match",
          "comparison_name": "dob",
          "comparison_sort_order": 2,
          "comparison_vector_value": 0,
          "has_tf_adjustments": false,
          "is_null_level": false,
          "label_for_charts": "All other comparisons",
          "log2_bayes_factor": -2.114723717091652,
          "m_probability": 0.22653362300553073,
          "m_probability_description": "Amongst matching record comparisons, 22.65% of records are in the all other comparisons comparison level",
          "max_comparison_vector_value": 2,
          "probability_two_random_records_match": 0.02326254791526835,
          "sql_condition": "ELSE",
          "tf_adjustment_column": null,
          "tf_adjustment_weight": 1,
          "u_probability": 0.9811331331331331,
          "u_probability_description": "Amongst non-matching record comparisons, 98.11% of records are in the all other comparisons comparison level"
         },
         {
          "bayes_factor": 10.484858675056154,
          "bayes_factor_description": "If comparison level is `exact_match` then comparison is 10.48 times more likely to be a match",
          "comparison_name": "city",
          "comparison_sort_order": 3,
          "comparison_vector_value": 1,
          "has_tf_adjustments": true,
          "is_null_level": false,
          "label_for_charts": "exact_match",
          "log2_bayes_factor": 3.3902355104306197,
          "m_probability": 0.5782144900964232,
          "m_probability_description": "Amongst matching record comparisons, 57.82% of records are in the exact_match comparison level",
          "max_comparison_vector_value": 1,
          "probability_two_random_records_match": 0.02326254791526835,
          "sql_condition": "city_l = city_r",
          "tf_adjustment_column": "city",
          "tf_adjustment_weight": 1,
          "u_probability": 0.0551475711801453,
          "u_probability_description": "Amongst non-matching record comparisons, 5.51% of records are in the exact_match comparison level"
         },
         {
          "bayes_factor": 0.4464035832880252,
          "bayes_factor_description": "If comparison level is `all other comparisons` then comparison is  2.24 times less likely to be a match",
          "comparison_name": "city",
          "comparison_sort_order": 3,
          "comparison_vector_value": 0,
          "has_tf_adjustments": false,
          "is_null_level": false,
          "label_for_charts": "All other comparisons",
          "log2_bayes_factor": -1.1635794871398053,
          "m_probability": 0.4217855099035769,
          "m_probability_description": "Amongst matching record comparisons, 42.18% of records are in the all other comparisons comparison level",
          "max_comparison_vector_value": 1,
          "probability_two_random_records_match": 0.02326254791526835,
          "sql_condition": "ELSE",
          "tf_adjustment_column": null,
          "tf_adjustment_weight": 1,
          "u_probability": 0.9448524288198547,
          "u_probability_description": "Amongst non-matching record comparisons, 94.49% of records are in the all other comparisons comparison level"
         },
         {
          "bayes_factor": 263.2291676754963,
          "bayes_factor_description": "If comparison level is `exact_match` then comparison is 263.23 times more likely to be a match",
          "comparison_name": "email",
          "comparison_sort_order": 4,
          "comparison_vector_value": 1,
          "has_tf_adjustments": true,
          "is_null_level": false,
          "label_for_charts": "exact_match",
          "log2_bayes_factor": 8.04017554864013,
          "m_probability": 0.5774909200578013,
          "m_probability_description": "Amongst matching record comparisons, 57.75% of records are in the exact_match comparison level",
          "max_comparison_vector_value": 1,
          "probability_two_random_records_match": 0.02326254791526835,
          "sql_condition": "email_l = email_r",
          "tf_adjustment_column": "email",
          "tf_adjustment_weight": 1,
          "u_probability": 0.0021938713143283602,
          "u_probability_description": "Amongst non-matching record comparisons, 0.22% of records are in the exact_match comparison level"
         },
         {
          "bayes_factor": 0.4234380485302649,
          "bayes_factor_description": "If comparison level is `all other comparisons` then comparison is  2.36 times less likely to be a match",
          "comparison_name": "email",
          "comparison_sort_order": 4,
          "comparison_vector_value": 0,
          "has_tf_adjustments": false,
          "is_null_level": false,
          "label_for_charts": "All other comparisons",
          "log2_bayes_factor": -1.239777184635766,
          "m_probability": 0.42250907994219916,
          "m_probability_description": "Amongst matching record comparisons, 42.25% of records are in the all other comparisons comparison level",
          "max_comparison_vector_value": 1,
          "probability_two_random_records_match": 0.02326254791526835,
          "sql_condition": "ELSE",
          "tf_adjustment_column": null,
          "tf_adjustment_weight": 1,
          "u_probability": 0.9978061286856716,
          "u_probability_description": "Amongst non-matching record comparisons, 99.78% of records are in the all other comparisons comparison level"
         }
        ]
       },
       "resolve": {
        "axis": {
         "y": "independent"
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        "scale": {
         "y": "independent"
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       },
       "selection": {
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          "x"
         ],
         "type": "interval"
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       },
       "title": {
        "subtitle": "Use mousewheel to zoom",
        "text": "Model parameters (components of final match weight)"
       },
       "vconcat": [
        {
         "encoding": {
          "color": {
           "field": "log2_bayes_factor",
           "scale": {
            "domain": [
             -10,
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            ],
            "range": [
             "red",
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           },
           "title": "Match weight",
           "type": "quantitative"
          },
          "tooltip": [
           {
            "field": "comparison_name",
            "title": "Comparison name",
            "type": "nominal"
           },
           {
            "field": "probability_two_random_records_match",
            "format": ".4f",
            "title": "Probability two random records match",
            "type": "nominal"
           },
           {
            "field": "log2_bayes_factor",
            "format": ",.4f",
            "title": "Equivalent match weight",
            "type": "quantitative"
           },
           {
            "field": "bayes_factor_description",
            "title": "Match weight description",
            "type": "nominal"
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          ],
          "x": {
           "axis": {
            "domain": false,
            "labels": false,
            "ticks": false,
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           },
           "field": "log2_bayes_factor",
           "scale": {
            "domain": [
             -10,
             10
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           },
           "type": "quantitative"
          },
          "y": {
           "axis": {
            "title": "Prior (starting) match weight",
            "titleAlign": "right",
            "titleAngle": 0,
            "titleFontWeight": "normal"
           },
           "field": "label_for_charts",
           "sort": {
            "field": "comparison_vector_value",
            "order": "descending"
           },
           "type": "nominal"
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         },
         "height": 20,
         "mark": {
          "clip": true,
          "height": 15,
          "type": "bar"
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         "selection": {
          "zoom_selector": {
           "bind": "scales",
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         },
         "transform": [
          {
           "filter": "(datum.comparison_name == 'probability_two_random_records_match')"
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         ]
        },
        {
         "encoding": {
          "color": {
           "field": "log2_bayes_factor",
           "scale": {
            "domain": [
             -10,
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            "range": [
             "red",
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           },
           "title": "Match weight",
           "type": "quantitative"
          },
          "row": {
           "field": "comparison_name",
           "header": {
            "labelAlign": "left",
            "labelAnchor": "middle",
            "labelAngle": 0
           },
           "sort": {
            "field": "comparison_sort_order"
           },
           "type": "nominal"
          },
          "tooltip": [
           {
            "field": "comparison_name",
            "title": "Comparison name",
            "type": "nominal"
           },
           {
            "field": "label_for_charts",
            "title": "Label",
            "type": "ordinal"
           },
           {
            "field": "sql_condition",
            "title": "SQL condition",
            "type": "nominal"
           },
           {
            "field": "m_probability",
            "format": ".4f",
            "title": "M probability",
            "type": "quantitative"
           },
           {
            "field": "u_probability",
            "format": ".4f",
            "title": "U probability",
            "type": "quantitative"
           },
           {
            "field": "bayes_factor",
            "format": ",.4f",
            "title": "Bayes factor = m/u",
            "type": "quantitative"
           },
           {
            "field": "log2_bayes_factor",
            "format": ",.4f",
            "title": "Match weight = log2(m/u)",
            "type": "quantitative"
           },
           {
            "field": "bayes_factor_description",
            "title": "Match weight description",
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          ],
          "x": {
           "axis": {
            "title": "Comparison level match weight = log2(m/u)"
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           "field": "log2_bayes_factor",
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            "domain": [
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           },
           "type": "quantitative"
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          "y": {
           "axis": {
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           },
           "field": "label_for_charts",
           "sort": {
            "field": "comparison_vector_value",
            "order": "descending"
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           "type": "nominal"
          }
         },
         "height": {
          "step": 12
         },
         "mark": {
          "clip": true,
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         "resolve": {
          "axis": {
           "y": "independent"
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          "scale": {
           "y": "independent"
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         },
         "selection": {
          "zoom_selector": {
           "bind": "scales",
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           "type": "interval"
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         },
         "transform": [
          {
           "filter": "(datum.comparison_name != 'probability_two_random_records_match')"
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         ]
        }
       ]
      },
      "image/png": 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",
      "text/plain": [
       "<VegaLite 4 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linker.match_weights_chart()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "splink_demos",
   "language": "python",
   "name": "splink_demos"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}